Co-Founder & CPO, Rivvun AI
Ask a CPO or CFO whether savings leak between a signed contract and a completed payment, and few disagree. Rebates go unclaimed. Negotiated prices aren’t enforced on transactions and settlements. Credits are issued but never applied. Amendments change the commercial rules but never reach the transactions they govern.
Procurement negotiates the savings — prices, rebates, discounts, payment terms, service commitments — and enshrines them in contracts that live mostly as documents. Then transactions are executed and settlements are paid, and the settlement is rarely interrogated against the depth of the contract terms.
Leakage is not the debate.
The savings leak in that handoff. It’s why negotiated savings and realized savings are rarely the same number.
The debate is never whether leakage exists — it’s what it takes to find, validate and capture it. And the moment that feels like a big IT transformation, it’s too complex to start, so nobody does.
The wrong assumption causing inaction
Procurement and finance leaders are right to be cautious. They’ve had earlier experiences of IT initiatives that promised savings and turned into multi-year projects. Now new AI initiatives are on the table — but leaders are being told that realizing value requires fundamental redesign and clean, accurate data first. So, the opportunity stalls, and the money stays on the table.
We want to get savings by finding the leakage seeping through the cracks — not by rebuilding the wall.
You find it by looking at historical data, validating the savings opportunities, and taking the quick wins. Then you let the economics decide whether to bring the wall down and rebuild it at all. Redesign and transformation take years to execute and realize value. The savings are there to grab right now.
Start with a bounded question — usually a hypothesis about where leakage is likely — and you immediately know which data to look at. That data already exists in your enterprise systems.
Didn’t we spend the last decade running digitization projects? What we never built is the seam that runs through all that data, binds it, and surfaces the insight.
That gap — the seam the digitization projects left behind — is exactly what AI agents can now fill: stitching the data together and validating your hypothesis.
The opportunity in not having clean data
Here’s the part worth considering: the same fragmentation that makes leakage likely is exactly what AI agents are built to work through.
A bounded scope with a few precise questions is the way to test a hypothesis. Did invoices for this category match contracted pricing last quarter? Were eligible rebates for this supplier group calculated and claimed? Were issued credits applied?
The data to answer this already exists — contracts, POs, invoices, payments, credits, operational records. Begin with read-only extracts from the systems you already use. The data will be fragmented: supplier names inconsistent, pricing and rebate terms buried in exhibits, no link between a PO and its contract or an invoice and its terms.
This is what we built Rivvun to do — with a purpose-built data foundation and domain playbooks, our agents extract the contractual terms, normalize the records, connect clauses to transactions, and surface each exception with its source evidence, amount and confidence, without your team building a data warehouse first.
That’s the real opportunity. You start the journey toward clean, connected data and find savings while you’re at it. Any future transformation or redesign will need this foundation anyway — and you can’t afford to wait years to build it before you see a dollar.
None of this runs without control: read-only access, every finding traced to source, materiality and confidence thresholds, and policy-based approval before any supplier-facing action. When the evidence sits in the data, the next action — the claim, the credit, the conversation with the supplier — becomes far easier to defend.
Find savings, then scale.
CPOs and CFOs don’t need another slide with a large theoretical number. They need to know which savings are valid, material and achievable — and who owns delivering them.
This reverses the usual order.
Instead of funding infrastructure in the hope that savings emerge, you prove and bank savings first — and let those savings fund the selective integration and continuous control that follow.
Make your move now.
Don’t ask whether the whole data estate is ready. Ask whether one bounded scope can produce one defensible saving. Choose a category, supplier group or leakage pattern; a defined period; controlled access to the relevant records; an accountable owner across procurement and finance; and the materiality, approval and claim rules, set before you start. Then measure what was identified, validated, realized and prevented.
Once that’s demonstrated, scale becomes an investment decision — not an act of faith.
At Rivvun, that first bounded scope is exactly where we start. If you’re curious what it would surface in your own data, let’s talk.